Open AccessAutomated segmentation of peripapillary retinal boundaries in OCT combining a convolutional neural network and a multi-weights graph search
Author(s) -
Pengxiao Zang,
Jie Wang,
Tristan T. Hormel,
Liang Liu,
David Huang,
Yali Jia
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.004340
Subject(s) - segmentation , optical coherence tomography , computer science , artificial intelligence , retinal , convolutional neural network , ground truth , sørensen–dice coefficient , pattern recognition (psychology) , image segmentation , optic disc , graph , optic disk , computer vision , optics , ophthalmology , physics , medicine , theoretical computer science
Quantitative analysis of the peripapillary retinal layers and capillary plexuses from optical coherence tomography (OCT) and OCT angiography images depend on two segmentation tasks - delineating the boundary of the optic disc and delineating the boundaries between retinal layers. Here, we present a method combining a neural network and graph search to perform these two tasks. A comparison of this novel method's segmentation of the disc boundary showed good agreement with the ground truth, achieving an overall Dice similarity coefficient of 0.91 ± 0.04 in healthy and glaucomatous eyes. The absolute error of retinal layer boundaries segmentation in the same cases was 4.10 ± 1.25 µm.